Results

Notes / Questions

• The complete dataset includes 235 observations

  • excluding region NA there are 201 observations left

  • excluding incomplete responses there are 199 observations left

  • excluding high income countries there are 176 observations left

Figure 1

Identifying populations for latent TB infection (LTBI) testing and TB preventive treatment after exclusion of active TB. Mapping according to WHO guidelines (see WHO consolidated guidelines, Table 1, page xi).

df1 <- df %>%
  select(
    record_id, region, tpt_eligible_adult___6, tpt_lt12m, tpt_ge1y, tpt_g11y_tb, tpt_peds_lt5,
    tpt_peds_ge9, tpt_adults, tpt_hrcontacts, tpt_atrisk___3:tpt_atrisk___13, HBC_ANY, HBC_TB,
    HBC_TB_HIV, HBC_DR_TB, tbhiv_integration.factor, integration_oth, Income_group, Prev_cat.factor) %>% 
  mutate(
    across(
      c(tpt_lt12m, tpt_g11y_tb, tpt_peds_lt5, tpt_ge1y, tpt_peds_ge9, 
        tpt_adults,tpt_hrcontacts),
      ~ as.factor(if_else(is.na(.), "0", as.character(.)))), #manually checked - they dont provide TPT at all, so would be "No" but they had to skip the question
    across(
      c(tpt_peds_lt5, tpt_peds_ge9, tpt_adults, tpt_hrcontacts),
      ~ as.factor(if_else(. == "2", "0", .)))) %>% #"No" was coded as 2 here
  rename(
    '< 5 years old' = tpt_peds_lt5,
    '5-10 years old' = tpt_peds_ge9,
    '> 10 years old' = tpt_adults,
    'Contacts of MDR-TB Patients' = tpt_hrcontacts,
    'People on anti-TNF treatment' = tpt_atrisk___3,
    'People preparing for transplant' = tpt_atrisk___4,
    'People with silicosis' = tpt_atrisk___5,
    Prisoners = tpt_atrisk___6,
    'Health workers' = tpt_atrisk___7,
    'Immigrants (TB burden country)' = tpt_atrisk___8,
    Homeless = tpt_atrisk___9,
    'Drug users' = tpt_atrisk___10,
    'Alcohol users' = tpt_atrisk___11,
    'Tabacco users' = tpt_atrisk___12,
    'Underweight' = tpt_atrisk___13,
    ' > 10 years old' = tpt_eligible_adult___6, #add space to distingush it from the same variable in the other category
    'Infants (in contact with person with TB)' = tpt_lt12m,
    '> 10 years old (high TB setting)' = tpt_ge1y,
    'children LWH (completed TB treatment)' = tpt_g11y_tb,
    'Level of integration' = tbhiv_integration.factor,
    'Income Level' = Income_group, 
    'HIV Prevalence' = Prev_cat.factor) %>% 
  mutate(across(
    .cols = everything(),
    .fns = ~ as.factor(as.character(.)))) %>% 
  pivot_longer(
    cols = 
      -c(record_id, region, HBC_ANY, HBC_TB, HBC_TB_HIV, HBC_DR_TB, `Level of integration`,
         integration_oth, `Income Level`, `HIV Prevalence`),
    names_to = "variable",
    values_to = "category") %>% 
  mutate(
    category = factor(category, levels = c("0", "1", "3", "77"), 
                      labels = c("No", "Yes", "Don't know", "Not available")),
    who_group = factor(
      case_when(
        variable %in% c(" > 10 years old", #add a space to distinguish it from the same variable in the other category
                        "Infants (in contact with person with TB)", 
                        "> 10 years old (high TB setting)", 
                        "children LWH (completed TB treatment)") ~ "PLHIV",
        variable %in% c("< 5 years old",
                        "5-10 years old",
                        "> 10 years old",
                        "Contacts of MDR-TB Patients") ~ "Household contacts",
        variable %in% c("People on anti-TNF treatment",
                        "People preparing for transplant",
                        "Prisoners",
                        "People with silicosis",
                        "Health workers",
                        "Immigrants (TB burden country)",
                        "Homeless",
                        "Drug users",
                        "Alcohol users",
                        "Tabacco users",
                        "Underweight") ~ "Other people at risk", 
        TRUE ~ "Others"),
      levels = c("PLHIV", "Household contacts", "Other people at risk")))

fig1("")

Stratified by high burden countries (all)

fig1("HBC")

Stratified by high burden countries (any vs. no)

fig1("HBC_ANY")

Stratified by region

fig1("region")

Stratified by level of integration

fig1("Level of integration")

Note, there are only three clinics that responded “Other”. These are their written responses, they are excluded in the plot:

df1_int_OTH <- df1 %>% 
  filter(`Level of integration` == "Other") %>% 
  select(record_id, integration_oth) %>% 
  distinct()

df %>% filter(record_id %in% df1_int_OTH$record_id) %>% 
  select(name, country, region.factor) %>% 
  cbind(df1_int_OTH) %>%
  kable() %>% 
  kable_styling(full_width = T)

name	country	region.factor	record_id	integration_oth
Instituto Nacional de Ciencias Medicas y Nutricion Salvador Zubiran	MEX	CCASAnet (Latin America)	102	We start and follow up both, but the TB medication and DOT is provided in other clinics according where the patient lives
Kanyama	ZMB	Southern Africa	418	TB diagnosis in ART clinic(treatment in TB corner)
Centre Hospitalier Universitaire Gabriel Touré - Pediatric	MLI	West Africa	523	Soins du VIH a la clinique et traitement de la tuberculose hors site uniquement

Stratified by Income Level

fig1("Income Level")

Stratified by HIV Prevalence

fig1("HIV Prevalence")

Figure 2

Barriers to TPT administration: **11.15 What are the most important barriers to initiating patients with HIV on TPT at this facility? Check all that apply**  

df2 <- df %>% 
  select(record_id, region, tpt_barriers___77:tpt_barriers___88, HBC_ANY, HBC_TB,
    HBC_TB_HIV, HBC_DR_TB, tbhiv_integration.factor, Income_group, Prev_cat.factor) %>%
  rename(
         'No barriers' = tpt_barriers___77,
         'Medication availability' = tpt_barriers___1,
         'LTBI Testing stockouts' = tpt_barriers___2,
         'Availability of diagnostics active TB' = tpt_barriers___3,
         'Concerns drug resistance' = tpt_barriers___4,
         'Increased workload' = tpt_barriers___5,
         'Patients refuse TPT' = tpt_barriers___6,
         'Access to HIV care service delivery'= tpt_barriers___7,
         'Other barriers' = tpt_barriers___88,
         'Level of integration' = tbhiv_integration.factor,
         'Income Level' = Income_group, 
         'HIV Prevalence' = Prev_cat.factor) %>% 
  mutate(across(everything(), ~ as.factor(.))) %>% 
  pivot_longer(cols = -c(record_id, region, HBC_ANY, HBC_TB, HBC_TB_HIV, HBC_DR_TB, `Level of integration`, `Income Level`, `HIV Prevalence`),
               names_to = "variable",
               values_to = "category") %>% 
  mutate(variable = as.factor(variable),
         fct_relevel(variable, "No barriers"),
         category = as.factor(ifelse(category == 1, "Yes", "No")))

fig2("")

Figure 2: Proportion of clinics responding to the questions regarding specific boundaries to initiating patients with HIV on TPT at the clinic. Also, if somebody in the clinic received formal training for TPT.

Stratified by high burden countries

fig2("HBC")

Stratified by high burden countries (any vs. no)

fig2("HBC_ANY")

Stratified by region

fig2("region")

Stratified by level of integration

fig2("Level of integration")

Stratified by Income Level

fig2("Income Level")

Stratified by HIV Prevalence

fig2("HIV Prevalence")

Figure 3

• Proportion of sites who responded “Yes” to the following question: “11.6 Does this HIV clinic (or a co-located TB clinic) currently provide TB preventive therapy (TPT)?”.

• The points are located at the coordinates from the sites (from Google Maps).

coords <- read.csv("../data_clean/coordinates_sites.csv") %>%
  separate(coord, into = c("lat", "long"), sep = ",", convert = TRUE) %>% 
  filter(record_id %in% df$record_id)

world <- map_data("world") 

df3a <- df %>% 
  select(country, region_exact, tpt_prov) %>% 
  group_by(region_exact) %>% 
  summarise(prop_tpt = mean(tpt_prov == 1, na.rm = TRUE))

df3 <- df %>% 
  select(record_id, country, region, region_exact)  %>% 
  left_join(df3a, by = "region_exact") %>%   
  mutate(region = case_when(
    country == "ARG" ~ "Argentina",
    country == "AUS" ~ "Australia",
    country == "BDI" ~ "Burundi",
    country == "BEN" ~ "Benin",
    country == "BFA" ~ "Burkina Faso",
    country == "BRA" ~ "Brazil",
    country == "CHL" ~ "Chile",
    country == "CHN" ~ "China",
    country == "CIV" ~ "Ivory Coast",
    country == "CMR" ~ "Cameroon",
    country == "COD" ~ "Democratic Republic of the Congo",
    country == "COG" ~ "Republic of the Congo",
    country == "GHA" ~ "Ghana",
    country == "HND" ~ "Honduras",
    country == "HTI" ~ "Haiti",
    country == "IDN" ~ "Indonesia",
    country == "IND" ~ "India",
    country == "JPN" ~ "Japan",
    country == "KEN" ~ "Kenya",
    country == "KHM" ~ "Cambodia",
    country == "KOR" ~ "South Korea",
    country == "LSO" ~ "Lesotho",
    country == "MEX" ~ "Mexico",
    country == "MLI" ~ "Mali",
    country == "MOZ" ~ "Mozambique",
    country == "MWI" ~ "Malawi",
    country == "MYS" ~ "Malaysia",
    country == "NGA" ~ "Nigeria",
    country == "PER" ~ "Peru",
    country == "PHL" ~ "Philippines",
    country == "RWA" ~ "Rwanda",
    country == "SGP" ~ "Singapore",
    country == "TGO" ~ "Togo",
    country == "THA" ~ "Thailand",
    country == "TWN" ~ "Taiwan",
    country == "TZA" ~ "Tanzania",
    country == "UGA" ~ "Uganda",
    country == "VNM" ~ "Vietnam",
    country == "ZAF" ~ "South Africa",
    country == "ZMB" ~ "Zambia",
    country == "ZWE" ~ "Zimbabwe",
    TRUE ~ NA_character_))  
  
worldSubset <- left_join(world, df3, by = "region", relationship =  "many-to-many") %>% 
  filter(region != "Antarctica")

plain <- theme(
  axis.text = element_blank(),
  axis.line = element_blank(),
  axis.ticks = element_blank(),
  panel.border = element_blank(),
  panel.grid = element_blank(),
  axis.title = element_blank(),
  panel.background = element_rect(fill = "white"),
  plot.title = element_text(hjust = 0.5))

my_colors <- c("value1" = "#color1", 
               "value2" = "#color2",
               "value3" = "#color3",
               "value4" = "#color4",
               "value5" = "#color5")  # Add a color for NA values

worldSubset %>% 
  ggplot(aes(x = long, y = lat)) +
  coord_fixed(1.3) +
  geom_polygon(aes(fill = prop_tpt, group = group)) +
  scale_fill_gradient(low = "#D8F3DC", high = "#3DAC78", limits = c(0.9, 1), na.value = "#CECECD") +
  geom_point(data = coords, aes(x = long, y = lat), color = wes_palette("GrandBudapest1")[2], size = .7, position = "jitter") +
  plain +
  labs(fill = "", x = "", y = "")

Figure 4

Numbers of newly initiated TPT and newly intitated ART in a subset of sites, 2018-2022 (data from COVID-TB survey, MR214).

• Nicolas will provide the figure

Figure 5

Available treatment options of TPT regimens.

• This is the table of the survey for reference.

  

fig5("")

Supplementary table

I can change the order of the table for the final version (as proposed by Lukas) i.e. categories as columns. I omitted this for the moment because this option here is easier to code.

table1(~ `6-month isoniazid (6H)` + `9-month isoniazid (9H)` + `12-month isoniazid (12H)` + `36/Lifetime isoniazid (36/Lifetime H)` + `3-month rifampicin (3R)` + `4-month rifampicin (4R)` +  `3-month isoniazid-rifampicin (3HR)` + `Once-weekly isoniazid-rifapentine for 12 weeks (3HP)` + `Once-daily isoniazid-rifapentine for 1 month (1HP)` + `Regimens for MDR-TB exposure` + Other | HBC_ANY, 
       overall = c(left = "All"),
       data = df5,
       topclass="Rtable1-zebra",
       caption = "Available treatment options of TPT regimens")

Available treatment options of TPT regimens

	All (N=176)	HBC (N=138)	Not HBC (N=38)
6-month isoniazid (6H)
Adults	29 (16.5%)	26 (18.8%)	3 (7.9%)
Both	91 (51.7%)	72 (52.2%)	19 (50.0%)
Children	33 (18.8%)	24 (17.4%)	9 (23.7%)
Don't know	2 (1.1%)	1 (0.7%)	1 (2.6%)
None	21 (11.9%)	15 (10.9%)	6 (15.8%)
9-month isoniazid (9H)
Adults	13 (7.4%)	10 (7.2%)	3 (7.9%)
Both	6 (3.4%)	4 (2.9%)	2 (5.3%)
Children	1 (0.6%)	0 (0%)	1 (2.6%)
Don't know	11 (6.3%)	9 (6.5%)	2 (5.3%)
None	145 (82.4%)	115 (83.3%)	30 (78.9%)
12-month isoniazid (12H)
Adults	8 (4.5%)	8 (5.8%)	0 (0%)
Both	2 (1.1%)	2 (1.4%)	0 (0%)
Children	1 (0.6%)	0 (0%)	1 (2.6%)
Don't know	11 (6.3%)	9 (6.5%)	2 (5.3%)
None	154 (87.5%)	119 (86.2%)	35 (92.1%)
36/Lifetime isoniazid (36/Lifetime H)
Adults	2 (1.1%)	2 (1.4%)	0 (0%)
Both	1 (0.6%)	0 (0%)	1 (2.6%)
Children	1 (0.6%)	1 (0.7%)	0 (0%)
Don't know	17 (9.7%)	14 (10.1%)	3 (7.9%)
None	155 (88.1%)	121 (87.7%)	34 (89.5%)
3-month rifampicin (3R)
Adults	5 (2.8%)	4 (2.9%)	1 (2.6%)
Both	3 (1.7%)	3 (2.2%)	0 (0%)
Children	3 (1.7%)	2 (1.4%)	1 (2.6%)
Don't know	14 (8.0%)	11 (8.0%)	3 (7.9%)
None	151 (85.8%)	118 (85.5%)	33 (86.8%)
4-month rifampicin (4R)
Adults	4 (2.3%)	4 (2.9%)	0 (0%)
Both	5 (2.8%)	5 (3.6%)	0 (0%)
Children	3 (1.7%)	3 (2.2%)	0 (0%)
Don't know	14 (8.0%)	11 (8.0%)	3 (7.9%)
None	150 (85.2%)	115 (83.3%)	35 (92.1%)
3-month isoniazid-rifampicin (3HR)
Adults	16 (9.1%)	13 (9.4%)	3 (7.9%)
Both	15 (8.5%)	12 (8.7%)	3 (7.9%)
Children	18 (10.2%)	17 (12.3%)	1 (2.6%)
Don't know	12 (6.8%)	10 (7.2%)	2 (5.3%)
None	115 (65.3%)	86 (62.3%)	29 (76.3%)
Once-weekly isoniazid-rifapentine for 12 weeks (3HP)
Adults	79 (44.9%)	71 (51.4%)	8 (21.1%)
Both	26 (14.8%)	19 (13.8%)	7 (18.4%)
Children	5 (2.8%)	4 (2.9%)	1 (2.6%)
Don't know	5 (2.8%)	3 (2.2%)	2 (5.3%)
None	61 (34.7%)	41 (29.7%)	20 (52.6%)
Once-daily isoniazid-rifapentine for 1 month (1HP)
Adults	7 (4.0%)	6 (4.3%)	1 (2.6%)
Both	4 (2.3%)	2 (1.4%)	2 (5.3%)
Children	2 (1.1%)	2 (1.4%)	0 (0%)
Don't know	12 (6.8%)	8 (5.8%)	4 (10.5%)
None	151 (85.8%)	120 (87.0%)	31 (81.6%)
Regimens for MDR-TB exposure
Adults	3 (1.7%)	3 (2.2%)	0 (0%)
Both	6 (3.4%)	5 (3.6%)	1 (2.6%)
Children	4 (2.3%)	3 (2.2%)	1 (2.6%)
Don't know	19 (10.8%)	14 (10.1%)	5 (13.2%)
None	144 (81.8%)	113 (81.9%)	31 (81.6%)
Other
Both	2 (1.1%)	1 (0.7%)	1 (2.6%)
Children	1 (0.6%)	0 (0%)	1 (2.6%)
Don't know	21 (11.9%)	14 (10.1%)	7 (18.4%)
None	152 (86.4%)	123 (89.1%)	29 (76.3%)

Stratified by high burden countries

Caveat for all three stratification plots: These are the top 4 treatments overall now stratified, not necessarily the top 4 per stratum.

fig5("HBC")

Stratified by high burden countries (any vs. no)

fig5("HBC_ANY")

Stratified by region

fig5("region")

Stratified by level of integration

fig5("Level of integration")

Stratified by Income Level

fig5("Income Level")

Stratified by HIV Prevalence

fig5("HIV Prevalence")

Table 1

df6 <- df %>% 
  select(record_id, region, HBC_ANY, adultped.factor, rural.factor, level.factor, tbtrt.factor, tbhiv_integration.factor)  %>% 
  rename(`Population the center serves` = adultped.factor,
         `Facility location` = rural.factor,
         `Facility level of care`  = level.factor,
         `TB treatment for PLHIV` = tbtrt.factor,
         `Level of integrated TB/HIV services` = tbhiv_integration.factor) %>% 
    mutate(`Facility level of care` = fct_drop(`Facility level of care`),
           `Facility location` = fct_drop(`Facility location`))

levels(df6$`Level of integrated TB/HIV services`) <- c("Full-integration (TB diagnostics and TB treatment)", "Partial integration (TB diagnostics or TB treatment)", "Not integrated", "Other")

levels(df6$`Population the center serves`) <- c("Adults", "Children", "Both")

table1(~ `Population the center serves` + `Facility location` + `Facility level of care` + `TB treatment for PLHIV` + `Level of integrated TB/HIV services` |  HBC_ANY + region, data = df6,
       overall = c(left = "All"),
       topclass="Rtable1-zebra",
       caption = "Site characteristics stratified by region and by classification in High Burden Countries (HBC) for in any of the three categories according to WHO definition.")

Site characteristics stratified by region and by classification in High Burden Countries (HBC) for in any of the three categories according to WHO definition.

	All			HBC			Not HBC
	Africa (N=145)	Asia-Pacific (N=23)	Latin America (N=8)	Africa (N=115)	Asia-Pacific (N=19)	Latin America (N=4)	Africa (N=30)	Asia-Pacific (N=4)	Latin America (N=4)
Population the center serves
Adults	12 (8.3%)	15 (65.2%)	4 (50.0%)	6 (5.2%)	12 (63.2%)	2 (50.0%)	6 (20.0%)	3 (75.0%)	2 (50.0%)
Children	12 (8.3%)	4 (17.4%)	2 (25.0%)	3 (2.6%)	4 (21.1%)	2 (50.0%)	9 (30.0%)	0 (0%)	0 (0%)
Both	118 (81.4%)	4 (17.4%)	2 (25.0%)	103 (89.6%)	3 (15.8%)	0 (0%)	15 (50.0%)	1 (25.0%)	2 (50.0%)
Missing	3 (2.1%)	0 (0%)	0 (0%)	3 (2.6%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)
Facility location
Urban	62 (42.8%)	19 (82.6%)	8 (100%)	40 (34.8%)	17 (89.5%)	4 (100%)	22 (73.3%)	2 (50.0%)	4 (100%)
Mostly urban	16 (11.0%)	3 (13.0%)	0 (0%)	10 (8.7%)	1 (5.3%)	0 (0%)	6 (20.0%)	2 (50.0%)	0 (0%)
Mostly rural	22 (15.2%)	0 (0%)	0 (0%)	22 (19.1%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)
Rural	43 (29.7%)	1 (4.3%)	0 (0%)	41 (35.7%)	1 (5.3%)	0 (0%)	2 (6.7%)	0 (0%)	0 (0%)
Missing	2 (1.4%)	0 (0%)	0 (0%)	2 (1.7%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)
Facility level of care
Health centre	91 (62.8%)	4 (17.4%)	0 (0%)	73 (63.5%)	4 (21.1%)	0 (0%)	18 (60.0%)	0 (0%)	0 (0%)
District hospital	16 (11.0%)	0 (0%)	0 (0%)	16 (13.9%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)
Regional, provincial or university hospital	30 (20.7%)	19 (82.6%)	8 (100%)	18 (15.7%)	15 (78.9%)	4 (100%)	12 (40.0%)	4 (100%)	4 (100%)
Missing	8 (5.5%)	0 (0%)	0 (0%)	8 (7.0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)
TB treatment for PLHIV
Provided in HIV Clinic	91 (62.8%)	12 (52.2%)	6 (75.0%)	79 (68.7%)	10 (52.6%)	2 (50.0%)	12 (40.0%)	2 (50.0%)	4 (100%)
In same health facility (but not at HIV clinic)	44 (30.3%)	8 (34.8%)	2 (25.0%)	33 (28.7%)	8 (42.1%)	2 (50.0%)	11 (36.7%)	0 (0%)	0 (0%)
Only offsite (referral)	9 (6.2%)	3 (13.0%)	0 (0%)	3 (2.6%)	1 (5.3%)	0 (0%)	6 (20.0%)	2 (50.0%)	0 (0%)
Not available	1 (0.7%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	1 (3.3%)	0 (0%)	0 (0%)
Level of integrated TB/HIV services
Full-integration (TB diagnostics and TB treatment)	94 (64.8%)	11 (47.8%)	3 (37.5%)	79 (68.7%)	9 (47.4%)	2 (50.0%)	15 (50.0%)	2 (50.0%)	1 (25.0%)
Partial integration (TB diagnostics or TB treatment)	39 (26.9%)	11 (47.8%)	4 (50.0%)	30 (26.1%)	9 (47.4%)	2 (50.0%)	9 (30.0%)	2 (50.0%)	2 (50.0%)
Not integrated	10 (6.9%)	1 (4.3%)	0 (0%)	5 (4.3%)	1 (5.3%)	0 (0%)	5 (16.7%)	0 (0%)	0 (0%)
Other	2 (1.4%)	0 (0%)	1 (12.5%)	1 (0.9%)	0 (0%)	0 (0%)	1 (3.3%)	0 (0%)	1 (25.0%)

Original factor levels Level of integrated TB/HIV services:

• [1] “Full-integration (TB diagnostics and TB treatment under same roof or same facility than HIV care)”

• [2] “Partial integration (either TB diagnostics or TB treatment available under same roof or same facility than HIV care)”

• [3] “Not integrated”

• [4] “Other, specify: {integration_oth}”

Table 2

df_ltbi_testing <- df %>% 
  select(record_id, ltbi_testing___0, ltbi_testing___1, ltbi_testing___2, ltbi_testing___88, ltbi_testing_oth)  %>% 
  mutate(`Ltbi testing` = case_when(ltbi_testing___0 == 1 ~ "None",
                                    ltbi_testing___1 == 1 & ltbi_testing___2 == 1 ~ "Both",
                                    ltbi_testing___1 == 1 ~ "Interferon-Gamma Release Assays (IGRAs) blood test",
                                    ltbi_testing___2 == 1 ~ "Tuberculin Skin Test (TST)",
                                    ltbi_testing_oth != "" ~ "Other",
                                    TRUE ~ NA)) %>% 
  select(record_id, `Ltbi testing`)

df7 <- df %>% 
  select(record_id, region, HBC_ANY, tb_symptom_screen.factor, screen_cough.factor, screen_fever.factor, screen_nightsweats.factor, screen_weightloss.factor, screen_contact.factor, screen_fatigue.factor, screen_oth.factor, ltbi_ligibility.factor, tpt_prov.factor, tpt_training.factor) %>% 
  left_join(df_ltbi_testing, by = "record_id") %>% 
  rename(`TB symptom screening for enrolled patients?` = tb_symptom_screen.factor,
         `Cough screening` = screen_cough.factor,
         `Fever screening`  = screen_fever.factor,
         `Night sweats screening` = screen_nightsweats.factor,
         `Weight loss screening` = screen_weightloss.factor,
         `History of contact with a TB patient screening` = screen_contact.factor,
         `Fatigue/lethargy (or decreased playfulness in children) screening` = screen_fatigue.factor,
         Other = screen_oth.factor,
         `LTBI to check eligibility of PLHIV to receive TPT`= ltbi_ligibility.factor,
         `TPT currently provided in-house or at co-located TB clinic` = tpt_prov.factor,
         `Anyone attended a formal training on TPT provision` = tpt_training.factor)  

levels(df7$`TB symptom screening for enrolled patients?`) <- c("Yes, at the time of enrollment into HIV care at this facility only", "Yes, at every appointment", "Other", "No")

table1(~ `TB symptom screening for enrolled patients?` + `Cough screening` + `Fever screening` + `Night sweats screening` + `Weight loss screening` + `History of contact with a TB patient screening` + `Fatigue/lethargy (or decreased playfulness in children) screening` + Other +`LTBI to check eligibility of PLHIV to receive TPT` + `TPT currently provided in-house or at co-located TB clinic` + `Anyone attended a formal training on TPT provision`|  HBC_ANY + region, data = df7,
       overall = c(left = "All"),
       topclass="Rtable1-zebra",
       caption = "Availability of screening and testing services stratified by region and by classification in High Burden Countries (HBC) in any of the three categories according to WHO definition.")

Availability of screening and testing services stratified by region and by classification in High Burden Countries (HBC) in any of the three categories according to WHO definition.

	All			HBC			Not HBC
	Africa (N=145)	Asia-Pacific (N=23)	Latin America (N=8)	Africa (N=115)	Asia-Pacific (N=19)	Latin America (N=4)	Africa (N=30)	Asia-Pacific (N=4)	Latin America (N=4)
TB symptom screening for enrolled patients?
Yes, at the time of enrollment into HIV care at this facility only	36 (24.8%)	12 (52.2%)	4 (50.0%)	30 (26.1%)	8 (42.1%)	2 (50.0%)	6 (20.0%)	4 (100%)	2 (50.0%)
Yes, at every appointment	108 (74.5%)	10 (43.5%)	3 (37.5%)	85 (73.9%)	10 (52.6%)	1 (25.0%)	23 (76.7%)	0 (0%)	2 (50.0%)
Other	1 (0.7%)	1 (4.3%)	1 (12.5%)	0 (0%)	1 (5.3%)	1 (25.0%)	1 (3.3%)	0 (0%)	0 (0%)
No	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)
Cough screening
Yes	145 (100%)	23 (100%)	8 (100%)	115 (100%)	19 (100%)	4 (100%)	30 (100%)	4 (100%)	4 (100%)
No	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)
Dont Know	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)
Fever screening
Yes	145 (100%)	23 (100%)	8 (100%)	115 (100%)	19 (100%)	4 (100%)	30 (100%)	4 (100%)	4 (100%)
No	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)
Dont Know	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)
Night sweats screening
Yes	145 (100%)	21 (91.3%)	8 (100%)	115 (100%)	17 (89.5%)	4 (100%)	30 (100%)	4 (100%)	4 (100%)
No	0 (0%)	2 (8.7%)	0 (0%)	0 (0%)	2 (10.5%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)
Dont Know	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)
Weight loss screening
Yes	145 (100%)	23 (100%)	8 (100%)	115 (100%)	19 (100%)	4 (100%)	30 (100%)	4 (100%)	4 (100%)
No	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)
Dont Know	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)
History of contact with a TB patient screening
Yes	139 (95.9%)	22 (95.7%)	6 (75.0%)	111 (96.5%)	18 (94.7%)	3 (75.0%)	28 (93.3%)	4 (100%)	3 (75.0%)
No	6 (4.1%)	1 (4.3%)	2 (25.0%)	4 (3.5%)	1 (5.3%)	1 (25.0%)	2 (6.7%)	0 (0%)	1 (25.0%)
Dont Know	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)
Fatigue/lethargy (or decreased playfulness in children) screening
Yes	127 (87.6%)	20 (87.0%)	7 (87.5%)	102 (88.7%)	18 (94.7%)	4 (100%)	25 (83.3%)	2 (50.0%)	3 (75.0%)
No	17 (11.7%)	3 (13.0%)	0 (0%)	12 (10.4%)	1 (5.3%)	0 (0%)	5 (16.7%)	2 (50.0%)	0 (0%)
Dont Know	1 (0.7%)	0 (0%)	1 (12.5%)	1 (0.9%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	1 (25.0%)
Other
Yes	30 (20.7%)	3 (13.0%)	1 (12.5%)	22 (19.1%)	3 (15.8%)	1 (25.0%)	8 (26.7%)	0 (0%)	0 (0%)
No	103 (71.0%)	14 (60.9%)	4 (50.0%)	85 (73.9%)	10 (52.6%)	2 (50.0%)	18 (60.0%)	4 (100%)	2 (50.0%)
Dont Know	12 (8.3%)	6 (26.1%)	3 (37.5%)	8 (7.0%)	6 (31.6%)	1 (25.0%)	4 (13.3%)	0 (0%)	2 (50.0%)
LTBI to check eligibility of PLHIV to receive TPT
Yes	45 (31.0%)	10 (43.5%)	4 (50.0%)	34 (29.6%)	9 (47.4%)	3 (75.0%)	11 (36.7%)	1 (25.0%)	1 (25.0%)
No	95 (65.5%)	12 (52.2%)	4 (50.0%)	77 (67.0%)	9 (47.4%)	1 (25.0%)	18 (60.0%)	3 (75.0%)	3 (75.0%)
Missing	5 (3.4%)	1 (4.3%)	0 (0%)	4 (3.5%)	1 (5.3%)	0 (0%)	1 (3.3%)	0 (0%)	0 (0%)
TPT currently provided in-house or at co-located TB clinic
Yes	140 (96.6%)	22 (95.7%)	8 (100%)	111 (96.5%)	18 (94.7%)	4 (100%)	29 (96.7%)	4 (100%)	4 (100%)
No	5 (3.4%)	1 (4.3%)	0 (0%)	4 (3.5%)	1 (5.3%)	0 (0%)	1 (3.3%)	0 (0%)	0 (0%)
Anyone attended a formal training on TPT provision
Yes	123 (84.8%)	22 (95.7%)	4 (50.0%)	96 (83.5%)	18 (94.7%)	2 (50.0%)	27 (90.0%)	4 (100%)	2 (50.0%)
No	15 (10.3%)	0 (0%)	1 (12.5%)	13 (11.3%)	0 (0%)	0 (0%)	2 (6.7%)	0 (0%)	1 (25.0%)
Dont Know	2 (1.4%)	0 (0%)	3 (37.5%)	2 (1.7%)	0 (0%)	2 (50.0%)	0 (0%)	0 (0%)	1 (25.0%)
Missing	5 (3.4%)	1 (4.3%)	0 (0%)	4 (3.5%)	1 (5.3%)	0 (0%)	1 (3.3%)	0 (0%)	0 (0%)

Table 3

Preparing outcome variable

df6 <- df1 %>% 
  group_by(record_id, who_group) %>% 
  summarize(any_yes = ifelse(any(category == "Yes"), 1, 0), .groups = 'drop') %>% 
  left_join(df %>% select(record_id, tbhiv_integration.factor, Income_group, Prev_cat.factor, HBC_ANY, tpt_training.factor, level.factor), by = "record_id") %>% 
  rename(
    'Level of integration' = tbhiv_integration.factor,
    'Income Level' = Income_group, 
    'HIV Prevalence' = Prev_cat.factor,
    'High burden country' = HBC_ANY,
    'TPT training' = tpt_training.factor,
    'Facility level of care' = level.factor) %>% 
  mutate(`Income Level` = as.factor(`Income Level`),
         `Level of integration` = case_when(
           `Level of integration` == "Other" ~ NA, 
           TRUE~`Level of integration`)) 

df6$`Level of integration` <- relevel(df6$`Level of integration`, ref = "No integration")
df6$`Income Level` <- relevel(df6$`Income Level`, ref = "Low income")
df6$`HIV Prevalence` <- relevel(df6$`HIV Prevalence`, ref = "low (<1%)")
df6$`High burden country` <- relevel(df6$`High burden country`, ref = "Not HBC")
df6$`TPT training` <- relevel(df6$`TPT training`, ref = "No")
df6$`Facility level of care` <- relevel(df6$`Facility level of care`, ref = "Regional, provincial or university hospital")

plhiv <- df6 %>% filter(who_group == "PLHIV")
household <- df6 %>% filter(who_group == "Household contacts")
oPaR <- df6 %>% filter(who_group == "Other people at risk")

Reference Levels:

• Level of integration: No integration

• Income Level: Low income

• HIV Prevalence: low (<1%)

• TPT training: No training

• acility level of care: Regional, provincial or university hospital

Outcome 1: Adherence to any of the recommendations for PLHIV.

m1 <- glm(any_yes ~ `Level of integration` + `Income Level` + `High burden country` + `TPT training` + `Facility level of care`,
          family = "binomial",
          data = plhiv)
summ(m1, confint = TRUE, exp = TRUE)

Observations	160 (16 missing obs. deleted)
Dependent variable	any_yes
Type	Generalized linear model
Family	binomial
Link	logit

𝛘²(9)	6.60
Pseudo-R² (Cragg-Uhler)	0.09
Pseudo-R² (McFadden)	0.07
AIC	103.58
BIC	134.34

	exp(Est.)	2.5%	97.5%	z val.	p
(Intercept)	4.71	0.19	114.57	0.95	0.34
Level of integrationFull integration	1.96	0.17	22.01	0.55	0.59
Level of integrationPartial integration	0.97	0.09	10.08	-0.03	0.98
Income LevelLower middle income	1.24	0.29	5.31	0.29	0.77
Income LevelUpper middle income	0.59	0.10	3.59	-0.58	0.57
High burden countryHBC	1.31	0.33	5.19	0.39	0.70
TPT trainingYes	1.01	0.11	9.15	0.01	0.99
TPT trainingDont Know	0.89	0.03	23.97	-0.07	0.94
Facility level of careHealth centre	1.52	0.41	5.68	0.62	0.53
Facility level of careDistrict hospital	11116418.89	0.00	Inf	0.01	0.99
Standard errors: MLE

Outcome 2: Adherence to any of the recommendations for Household contacts.

m2 <- glm(any_yes ~ `Level of integration` + `Income Level` + `High burden country` + `TPT training` + `Facility level of care`,
          family = "binomial",
          data = household)

## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred

summ(m2, confint = TRUE, exp = TRUE)

Observations	160 (16 missing obs. deleted)
Dependent variable	any_yes
Type	Generalized linear model
Family	binomial
Link	logit

𝛘²(9)	33.85
Pseudo-R² (Cragg-Uhler)	0.39
Pseudo-R² (McFadden)	0.31
AIC	94.50
BIC	125.26

	exp(Est.)	2.5%	97.5%	z val.	p
(Intercept)	12219564.46	0.00	Inf	0.01	0.99
Level of integrationFull integration	14.32	2.15	95.29	2.75	0.01
Level of integrationPartial integration	5.15	0.80	33.01	1.73	0.08
Income LevelLower middle income	0.20	0.04	1.10	-1.85	0.06
Income LevelUpper middle income	0.34	0.03	3.50	-0.91	0.36
High burden countryHBC	4.19	1.15	15.33	2.17	0.03
TPT trainingYes	0.00	0.00	Inf	-0.01	0.99
TPT trainingDont Know	9.26	0.00	Inf	0.00	1.00
Facility level of careHealth centre	2.28	0.64	8.12	1.27	0.20
Facility level of careDistrict hospital	45366090.11	0.00	Inf	0.01	0.99
Standard errors: MLE

Outcome 3: Adherence to any of the recommendations for other people at risk.

m3 <- glm(any_yes ~ `Level of integration` + `Income Level` + `High burden country` + `TPT training` + `Facility level of care`,
          family = "binomial",
          data = oPaR)
summ(m3, confint = TRUE, exp = TRUE)

Observations	160 (16 missing obs. deleted)
Dependent variable	any_yes
Type	Generalized linear model
Family	binomial
Link	logit

𝛘²(9)	52.85
Pseudo-R² (Cragg-Uhler)	0.38
Pseudo-R² (McFadden)	0.25
AIC	178.85
BIC	209.61

	exp(Est.)	2.5%	97.5%	z val.	p
(Intercept)	0.03	0.00	0.39	-2.72	0.01
Level of integrationFull integration	4.00	0.64	24.86	1.49	0.14
Level of integrationPartial integration	1.67	0.28	10.16	0.56	0.58
Income LevelLower middle income	3.29	1.32	8.19	2.56	0.01
Income LevelUpper middle income	0.74	0.23	2.35	-0.50	0.61
High burden countryHBC	3.02	1.22	7.47	2.39	0.02
TPT trainingYes	2.09	0.54	8.13	1.06	0.29
TPT trainingDont Know	8.89	0.65	121.52	1.64	0.10
Facility level of careHealth centre	3.36	1.43	7.85	2.79	0.01
Facility level of careDistrict hospital	33020546.90	0.00	Inf	0.02	0.99
Standard errors: MLE